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Anthony A. Philippakis

Bio: Anthony A. Philippakis is an academic researcher from Broad Institute. The author has contributed to research in topics: Medicine & Population. The author has an hindex of 28, co-authored 75 publications receiving 14900 citations. Previous affiliations of Anthony A. Philippakis include Massachusetts Institute of Technology & Harvard University.


Papers
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Journal ArticleDOI
TL;DR: A unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs is presented.
Abstract: Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.

10,056 citations

Journal ArticleDOI
Ryan E. Mills1, Klaudia Walter2, Chip Stewart3, Robert E. Handsaker4  +371 moreInstitutions (21)
03 Feb 2011-Nature
TL;DR: A map of unbalanced SVs is constructed based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations, and serves as a resource for sequencing-based association studies.
Abstract: Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

1,085 citations

Journal ArticleDOI
26 Jun 2009-Science
TL;DR: A broad survey of transcription factors reveals that related proteins can have multiple and differing DNA binding specificities, which may be important in gene regulation and in the evolution of transcriptional regulatory networks.
Abstract: Sequence preferences of DNA binding proteins are a primary mechanism by which cells interpret the genome. Despite the central importance of these proteins in physiology, development, and evolution, comprehensive DNA binding specificities have been determined experimentally for only a few proteins. Here, we used microarrays containing all 10-base pair sequences to examine the binding specificities of 104 distinct mouse DNA binding proteins representing 22 structural classes. Our results reveal a complex landscape of binding, with virtually every protein analyzed possessing unique preferences. Roughly half of the proteins each recognized multiple distinctly different sequence motifs, challenging our molecular understanding of how proteins interact with their DNA binding sites. This complexity in DNA recognition may be important in gene regulation and in the evolution of transcriptional regulatory networks.

966 citations

Journal ArticleDOI
TL;DR: A maximally compact, synthetic DNA sequence design for protein binding microarray (PBM) experiments that represents all possible DNA sequence variants of a given length k on a single, universal microarray that permits high-throughput interrogation of binding site preferences, including nucleotide interdependencies, at unprecedented resolution is presented.
Abstract: Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities

703 citations

Journal ArticleDOI
27 Jun 2008-Cell
TL;DR: A computational system is developed that successfully predicts binding sites for homeodomain proteins as distant from mouse as Drosophila and C. elegans, and the results provide an unprecedented level of resolution in the analysis of this simple domain structure and suggest that variation in sequence recognition may be a factor in its functional diversity and evolutionary success.

624 citations


Cited by
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Journal ArticleDOI
Adam Auton1, Gonçalo R. Abecasis2, David Altshuler3, Richard Durbin4  +514 moreInstitutions (90)
01 Oct 2015-Nature
TL;DR: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.
Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

12,661 citations

Journal ArticleDOI
Monkol Lek, Konrad J. Karczewski1, Konrad J. Karczewski2, Eric Vallabh Minikel1, Eric Vallabh Minikel2, Kaitlin E. Samocha, Eric Banks1, Timothy Fennell1, Anne H. O’Donnell-Luria1, Anne H. O’Donnell-Luria3, Anne H. O’Donnell-Luria2, James S. Ware, Andrew J. Hill1, Andrew J. Hill4, Andrew J. Hill2, Beryl B. Cummings2, Beryl B. Cummings1, Taru Tukiainen1, Taru Tukiainen2, Daniel P. Birnbaum1, Jack A. Kosmicki, Laramie E. Duncan2, Laramie E. Duncan1, Karol Estrada2, Karol Estrada1, Fengmei Zhao2, Fengmei Zhao1, James Zou1, Emma Pierce-Hoffman1, Emma Pierce-Hoffman2, Joanne Berghout5, David Neil Cooper6, Nicole A. Deflaux7, Mark A. DePristo1, Ron Do, Jason Flannick2, Jason Flannick1, Menachem Fromer, Laura D. Gauthier1, Jackie Goldstein1, Jackie Goldstein2, Namrata Gupta1, Daniel P. Howrigan2, Daniel P. Howrigan1, Adam Kiezun1, Mitja I. Kurki1, Mitja I. Kurki2, Ami Levy Moonshine1, Pradeep Natarajan, Lorena Orozco, Gina M. Peloso2, Gina M. Peloso1, Ryan Poplin1, Manuel A. Rivas1, Valentin Ruano-Rubio1, Samuel A. Rose1, Douglas M. Ruderfer8, Khalid Shakir1, Peter D. Stenson6, Christine Stevens1, Brett Thomas2, Brett Thomas1, Grace Tiao1, María Teresa Tusié-Luna, Ben Weisburd1, Hong-Hee Won9, Dongmei Yu, David Altshuler1, David Altshuler10, Diego Ardissino, Michael Boehnke11, John Danesh12, Stacey Donnelly1, Roberto Elosua, Jose C. Florez1, Jose C. Florez2, Stacey Gabriel1, Gad Getz2, Gad Getz1, Stephen J. Glatt13, Christina M. Hultman14, Sekar Kathiresan, Markku Laakso15, Steven A. McCarroll2, Steven A. McCarroll1, Mark I. McCarthy16, Mark I. McCarthy17, Dermot P.B. McGovern18, Ruth McPherson19, Benjamin M. Neale2, Benjamin M. Neale1, Aarno Palotie, Shaun Purcell8, Danish Saleheen20, Jeremiah M. Scharf, Pamela Sklar, Patrick F. Sullivan21, Patrick F. Sullivan14, Jaakko Tuomilehto22, Ming T. Tsuang23, Hugh Watkins16, Hugh Watkins17, James G. Wilson24, Mark J. Daly2, Mark J. Daly1, Daniel G. MacArthur2, Daniel G. MacArthur1 
18 Aug 2016-Nature
TL;DR: The aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC) provides direct evidence for the presence of widespread mutational recurrence.
Abstract: Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

8,758 citations

Journal ArticleDOI
01 Nov 2012-Nature
TL;DR: It is shown that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites.
Abstract: By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.

7,710 citations

Journal ArticleDOI
Heng Li1
TL;DR: Minimap2 is a general-purpose alignment program to map DNA or long mRNA sequences against a large reference database and is 3-4 times as fast as mainstream short-read mappers at comparable accuracy, and is ≥30 times faster than long-read genomic or cDNA mapper at higher accuracy, surpassing most aligners specialized in one type of alignment.
Abstract: Motivation Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms. Results Minimap2 is a general-purpose alignment program to map DNA or long mRNA sequences against a large reference database. It works with accurate short reads of ≥100 bp in length, ≥1 kb genomic reads at error rate ∼15%, full-length noisy Direct RNA or cDNA reads and assembly contigs or closely related full chromosomes of hundreds of megabases in length. Minimap2 does split-read alignment, employs concave gap cost for long insertions and deletions and introduces new heuristics to reduce spurious alignments. It is 3-4 times as fast as mainstream short-read mappers at comparable accuracy, and is ≥30 times faster than long-read genomic or cDNA mappers at higher accuracy, surpassing most aligners specialized in one type of alignment. Availability and implementation https://github.com/lh3/minimap2. Supplementary information Supplementary data are available at Bioinformatics online.

6,264 citations

Journal ArticleDOI
03 Apr 2015-Science
TL;DR: Treatment efficacy was associated with a higher number of mutations in the tumors, and a tumor-specific T cell response paralleled tumor regression in one patient, suggesting that the genomic landscape of lung cancers shapes response to anti–PD-1 therapy.
Abstract: Immune checkpoint inhibitors, which unleash a patient’s own T cells to kill tumors, are revolutionizing cancer treatment. To unravel the genomic determinants of response to this therapy, we used whole-exome sequencing of non–small cell lung cancers treated with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two independent cohorts, higher nonsynonymous mutation burden in tumors was associated with improved objective response, durable clinical benefit, and progression-free survival. Efficacy also correlated with the molecular smoking signature, higher neoantigen burden, and DNA repair pathway mutations; each factor was also associated with mutation burden. In one responder, neoantigen-specific CD8+ T cell responses paralleled tumor regression, suggesting that anti–PD-1 therapy enhances neoantigen-specific T cell reactivity. Our results suggest that the genomic landscape of lung cancers shapes response to anti–PD-1 therapy.

6,215 citations